CS Example: General Linear Test (cs2.sas) proc reg data=cs; model gpa=satm satv hsm hss hse; * test...
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Transcript of CS Example: General Linear Test (cs2.sas) proc reg data=cs; model gpa=satm satv hsm hss hse; * test...
CS Example: General Linear Test (cs2.sas)
proc reg data=cs; model gpa=satm satv hsm hss hse; * test H0: beta1 = beta2 = 0; sat: test satm, satv; * test H0: beta3=beta4=beta5=0; hs: test hsm, hss, hse;run;
CS Example: General Linear TestTest sat Results for Dependent Variable gpa
Source DF MeanSquare
F Value Pr > F
Numerator 2 0.46566 0.95 0.3882
Denominator 218 0.49000
Test hs Results for Dependent Variable gpa
Source DF MeanSquare
F Value Pr > F
Numerator 3 6.68660 13.65 <.0001
Denominator 218 0.49000
CS Example: General Linear Test
proc reg data=cs; model gpa=satm hsm hss hse; * test H0: beta1 = beta2 = 0; sat: test satm; * test H0: beta3=beta4=beta5=0; hs: test hsm, hss, hse;run;
Body Fat Example (nknw260.sas)
For 20 healthy female subjects between 25 – 30
Y = amount of body fat (fat)
X1 = tricepts skinfold thickness (skinfold)
X2 = thigh circumference (thigh)
X3 = midarm circumference (midarm)
Body Fat Example: Regression (input)
data bodyfat; infile 'I:\My Documents\Stat 512\CH07TA01.DAT'; input skinfold thigh midarm fat;proc print data=bodyfat; run;
proc reg data=bodyfat; model fat=skinfold thigh midarm;run;
Body Fat Example: Regression (output)Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 3 396.98461 132.32820 21.52 <.0001
Error 16 98.40489 6.15031
Corrected Total 19 495.38950
Root MSE 2.47998 R-Square 0.8014
Dependent Mean 20.19500 Adj R-Sq 0.7641
Coeff Var 12.28017
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 117.08469 99.78240 1.17 0.2578
skinfold 1 4.33409 3.01551 1.44 0.1699
thigh 1 -2.85685 2.58202 -1.11 0.2849
midarm 1 -2.18606 1.59550 -1.37 0.1896
Body Fat Example: Extra SSproc reg data=bodyfat; model fat=skinfold thigh midarm /ss1 ss2;run;
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 3 396.98461 132.32820 21.52 <.0001
Error 16 98.40489 6.15031
Corrected Total 19 495.38950
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t| Type I SS Type II SS
Intercept 1 117.08469 99.78240 1.17 0.2578 8156.76050 8.46816
skinfold 1 4.33409 3.01551 1.44 0.1699 352.26980 12.70489
thigh 1 -2.85685 2.58202 -1.11 0.2849 33.16891 7.52928
midarm 1 -2.18606 1.59550 -1.37 0.1896 11.54590 11.54590
Body Fat Example: Regression (output)Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 3 396.98461 132.32820 21.52 <.0001
Error 16 98.40489 6.15031
Corrected Total 19 495.38950
Root MSE 2.47998 R-Square 0.8014
Dependent Mean 20.19500 Adj R-Sq 0.7641
Coeff Var 12.28017
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 117.08469 99.78240 1.17 0.2578
skinfold 1 4.33409 3.01551 1.44 0.1699
thigh 1 -2.85685 2.58202 -1.11 0.2849
midarm 1 -2.18606 1.59550 -1.37 0.1896
Body Fat Example: Correlationproc corr data=bodyfat noprob;run;
Pearson Correlation Coefficients, N = 20
skinfold thigh midarm fat
skinfold 1.00000 0.92384 0.45778 0.84327
thigh 0.92384 1.00000 0.08467 0.87809
midarm 0.45778 0.08467 1.00000 0.14244
fat 0.84327 0.87809 0.14244 1.00000
Body Fat Example: Single Xi’s (input)
proc reg data=bodyfat; model fat = skinfold; model fat = thigh; model fat = midarm;run;
Body Fat Example: Single Xi’s (output)Root MSE 2.81977
R-Square 0.7111
Adj R-Sq 0.6950
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -1.49610 3.31923 -0.45 0.6576
skinfold 1 0.85719 0.12878 6.66 <.0001
Root MSE 2.51024
R-Square 0.7710
Adj R-Sq 0.7583
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -23.63449 5.65741 -4.18 0.0006
thigh 1 0.85655 0.11002 7.79 <.0001
Root MSE 5.19261
R-Square 0.0203
Adj R-Sq -0.0341
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 14.68678 9.09593 1.61 0.1238
midarm 1 0.19943 0.32663 0.61 0.5491
Body Fat Example: General Linear Test (input)
proc reg data=bodyfat; model fat=skinfold thigh midarm; thighmid: test thigh, midarm; skinmid: test skinfold, midarm; thigh: test thigh; skin: test skinfold;run;
Body Fat Example: General Linear Test (out)Test thighmid Results for Dependent Variable fat
Source DF MeanSquare
F Value Pr > F
Numerator 2 22.35741 3.64 0.0500
Denominator 16 6.15031
Test skinmid Results for Dependent Variable fat
Source DF MeanSquare
F Value Pr > F
Numerator 2 7.50940 1.22 0.3210
Denominator 16 6.15031
Test thigh Results for Dependent Variable fat
Source DF MeanSquare
F Value Pr > F
Numerator 1 7.52928 1.22 0.2849
Denominator 16 6.15031
Body Fat Example: Model Selection
Root MSE 2.47998
R-Square 0.8014
Adj R-Sq 0.7641
Root MSE 2.51024
R-Square 0.7710
Adj R-Sq 0.7583
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -23.63449 5.65741 -4.18 0.0006
thigh 1 0.85655 0.11002 7.79 <.0001
Root MSE 2.49628
R-Square 0.7862
Adj R-Sq 0.7610
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 6.79163 4.48829 1.51 0.1486
skinfold 1 1.00058 0.12823 7.80 <.0001
midarm 1 -0.43144 0.17662 -2.44 0.0258
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 117.08469 99.78240 1.17 0.2578
skinfold 1 4.33409 3.01551 1.44 0.1699
thigh 1 -2.85685 2.58202 -1.11 0.2849
midarm 1 -2.18606 1.59550 -1.37 0.1896
Coefficients of Partial Determination
1
2 1 2 3Y |23
2 3
SSM(X | X ,X )R
SSE(X ,X )
2
2 2 1 3Y |13
1 3
SSM(X | X ,X )R
SSE(X ,X )
2
2 3 1 2Y |1,2
1 2
SSM(X | X ,X )R
SSE(X ,X )
4
2 4 1 2 3Y |123
1 2 3
SSM(X | X ,X ,X )R
SSE(X ,X ,X )
Body Fat Example: Partial Correlation
proc reg data=bodyfat; model fat=skinfold thigh midarm / pcorr1 pcorr2;run;
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t| SquaredPartial
Corr Type I
SquaredPartial
Corr Type II
Intercept 1 117.08469 99.78240 1.17 0.2578 . .
skinfold 1 4.33409 3.01551 1.44 0.1699 0.71110 0.11435
thigh 1 -2.85685 2.58202 -1.11 0.2849 0.23176 0.07108
midarm 1 -2.18606 1.59550 -1.37 0.1896 0.10501 0.10501
Body Fat Example: Correlation (nknw260a.sas)
data bodyfat; infile 'I:\My Documents\Stat 512\CH07TA01.DAT'; input skinfold thigh midarm fat;proc print data=bodyfat; run;
data corbodyfat; set bodyfat; thmid = thigh + midarm;
proc reg data=corbodyfat; model fat = thmid thigh midarm;run;
Body Fat Example: Correlation
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 2 384.27972 192.13986 29.40 <.0001
Error 17 111.10978 6.53587
Corrected Total 19 495.38950
Body Fat Example: CorrelationNote: Model is not full rank. Least-squares solutions for the
parameters are not unique. Some statistics will be misleading. A reported DF of 0 or B means that the estimate is biased.
Note: The following parameters have been set to 0, since the variables are a linear combination of other variables as shown.
midarm = thmid - thigh
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 -25.99695 6.99732 -3.72 0.0017
thmid B 0.09603 0.16139 0.60 0.5597
thigh B 0.75485 0.20437 3.69 0.0018
midarm 0 0 . . .
Body Fat Example: Effects of Correlation
Variables in model b1 b2 s{b1} s{b2}
X1 0.8572 0.1288X2 0.8565 0.1100X1, X2 0.2224 0.6594 0.3034 0.2912X1, X2, X3 4.334 -2.857 3.013 2.582
Body Fat Example: Correlation (nknw260.sas)
proc corr data=bodyfat noprob;run;
Pearson Correlation Coefficients, N = 20
skinfold thigh midarm fat
skinfold 1.00000 0.92384 0.45778 0.84327
thigh 0.92384 1.00000 0.08467 0.87809
midarm 0.45778 0.08467 1.00000 0.14244
fat 0.84327 0.87809 0.14244 1.00000
Body Fat Example: Pairwise correlation
proc reg data=bodyfat corr; model fat=skinfold thigh midarm; model midarm = skinfold thigh; model skinfold = thigh midarm; model thigh = skinfold midarm;run;
Model R2
fat=skinfold thigh midarm 0.8014midarm = skinfold thigh 0.9904skinfold = thigh midarm 0.9986thigh = skinfold midarm 0.9982
Power Cell Example: (nknw302.sas)
Y: cycles until discharge – cyclesX1: charge rate (3 levels) – chrate
X2: temperature (3 levels) – tempdata powercell; infile 'I:\My Documents\Stat 512\CH07TA09.DAT'; input cycles chrate temp;proc print data=powercell; run;Obs cycles chrate temp
1 150 0.6 10
2 86 1.0 10
3 49 1.4 10
4 288 0.6 20 ⁞ ⁞ ⁞ ⁞
Power Cell Example: Multiple Regression
data powercell; set powercell; chrate2=chrate*chrate; temp2=temp*temp; ct=chrate*temp;
proc reg data=powercell; model cycles=chrate temp chrate2 temp2 ct / ss1 ss2;run;
Power Cell Example: Multiple Regression (cont)
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 5 55366 11073 10.57 0.0109
Error 5 5240.43860 1048.08772
Corrected Total 10 60606
Root MSE 32.37418 R-Square 0.9135
Dependent Mean 172.00000 Adj R-Sq 0.8271
Coeff Var 18.82220
Power Cell Example: Multiple Regression (cont)
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value Pr > |t|
Intercept 1 337.72149 149.96163 2.25 0.0741
chrate 1 -539.51754 268.86033 -2.01 0.1011
temp 1 8.91711 9.18249 0.97 0.3761
chrate2 1 171.21711 127.12550 1.35 0.2359
temp2 1 -0.10605 0.20340 -0.52 0.6244
ct 1 2.87500 4.04677 0.71 0.5092
Power Cell Example: Multiple Regression (cont)
Parameter EstimatesVariable DF Parameter
EstimateStandard
Errort Value Pr > |t| Type I SS Type II SS
Intercept 1 337.72149 149.96163 2.25 0.0741 325424 5315.62944
chrate 1 -539.51754 268.86033 -2.01 0.1011 18704 4220.41673
temp 1 8.91711 9.18249 0.97 0.3761 34202 988.38036
chrate2 1 171.21711 127.12550 1.35 0.2359 1645.96667 1901.19474
temp2 1 -0.10605 0.20340 -0.52 0.6244 284.92807 284.92807
ct 1 2.87500 4.04677 0.71 0.5092 529.00000 529.00000
Power Cell Example: Correlationsproc corr data=powercell noprob; var chrate temp chrate2 temp2 ct;run;
Pearson Correlation Coefficients, N = 11
chrate temp chrate2 temp2 ct
chrate 1.00000 0.00000 0.99103 0.00000 0.60532
temp 0.00000 1.00000 0.00000 0.98609 0.75665
chrate2 0.99103 0.00000 1.00000 0.00592 0.59989
temp2 0.00000 0.98609 0.00592 1.00000 0.74613
ct 0.60532 0.75665 0.59989 0.74613 1.00000
Power Cell Example: Centeringdata copy; set powercell; schrate=chrate; stemp=temp; drop chrate2 temp2 ct;
proc standard data=copy out=std mean=0; var schrate stemp;* schrate and stemp now have mean 0;proc print data=std;run;
Obs cycles chrate temp schrate stemp
1 150 0.6 10 -0.4 -10
2 86 1.0 10 0.0 -10
3 49 1.4 10 0.4 -10
4 288 0.6 20 -0.4 0
⁞ ⁞ ⁞ ⁞ ⁞ ⁞
Power Cell Example: Centered Variables
data std; set std; schrate2=schrate*schrate; stemp2=stemp*stemp; sct=schrate*stemp;
proc reg data=std; model cycles= chrate temp schrate2 stemp2 sct / ss1 ss2;
Power Cell Example: Centered Variables (cont)
Parameter EstimatesVariable DF Parameter
EstimateStandard
Errort Value Pr > |t|
Intercept 1 151.42544 45.45653 3.33 0.0208
chrate 1 -139.58333 33.04176 -4.22 0.0083
temp 1 7.55000 1.32167 5.71 0.0023
schrate2 1 171.21711 127.12550 1.35 0.2359
stemp2 1 -0.10605 0.20340 -0.52 0.6244
sct 1 2.87500 4.04677 0.71 0.5092
Power Cell Example: Centered Variables (cont)
Parameter EstimatesVariable DF Parameter
EstimateStandard
Errort Value Pr > |t| Type I SS Type II SS
Intercept 1 151.42544 45.45653 3.33 0.0208 325424 11631
chrate 1 -139.58333 33.04176 -4.22 0.0083 18704 18704
temp 1 7.55000 1.32167 5.71 0.0023 34202 34202
schrate2 1 171.21711 127.12550 1.35 0.2359 1645.96667 1901.19474
stemp2 1 -0.10605 0.20340 -0.52 0.6244 284.92807 284.92807
sct 1 2.87500 4.04677 0.71 0.5092 529.00000 529.00000
Power Cell Example: Centered Variables (cont)
proc corr data=std noprob;var chrate temp schrate2 stemp2 sct;
run;
Pearson Correlation Coefficients, N = 11
chrate temp schrate2 stemp2 sct
chrate 1.00000 0.00000 0.00000 0.00000 0.00000
temp 0.00000 1.00000 0.00000 0.00000 0.00000
schrate2 0.00000 0.00000 1.00000 0.26667 0.00000
stemp2 0.00000 0.00000 0.26667 1.00000 0.00000
sct 0.00000 0.00000 0.00000 0.00000 1.00000
Power Cell Example: Second Orderproc reg data=std; model cycles= chrate temp schrate2 stemp2 sct / ss1 ss2;
second: test schrate2, stemp2, sct;run;
Test second Results for Dependent Variable cycles
Source DF MeanSquare
F Value Pr > F
Numerator 3 819.96491 0.78 0.5527
Denominator 5 1048.08772
Insurance Example: Background (nknw459.sas)
Y: number of months for an insurance company to adopt an innovation
X1: size of the firm
X2: Type of firm
X2 = 0 mutual fund firm
X2 = 1 stock firm
Questions1) Do stock firms adopt innovation faster?2) Does the size of the firm have an effect on 1)?
Insurance Example: Inputdata insurance; infile 'I:\My Documents\Stat 512\CH11TA01.DAT'; input months size stock;proc print data=insurance;run;
Obs months size stock1 17 151 02 26 92 0⁞ ⁞ ⁞ ⁞19 30 124 120 14 246 1
Insurance Example: Scatterplotsymbol1 v=M i=sm70 c=black l=1;symbol2 v=S i=sm70 c=red l=3;title1 h=3 'Insurance Innovation';axis1 label=(h=2);axis2 label=(h=2 angle=90);proc sort data=insurance;
by stock size;title2 h=2 'with smoothed lines';proc gplot data=insurance; plot months*size=stock/haxis=axis1 vaxis=axis2;run;
Insurance Example: Regressiondata insurance; set insurance; sizestock=size*stock;run;
proc reg data=insurance; model months = size stock sizestock; sameline: test stock, sizestock;run;
Insurance Example: Regression (cont)
Test sameline Results for Dependent Variable months
Source DFMean
SquareF Value Pr > F
Numerator 2 158.12584 14.34 0.0003Denominator 16 11.02381
Analysis of Variance
Source DFSum of
SquaresMean
SquareF Value Pr > F
Model 3 1504.41904 501.47301 45.49 <.0001Error 16 176.38096 11.02381 Corrected Total 19 1680.80000
Root MSE 3.32021 R-Square 0.8951Dependent Mean 19.40000 Adj R-Sq 0.8754
Insurance Example: Regression (cont)
Parameter Estimates
Variable DFParameter
EstimateStandard
Errort Value Pr > |t|
Intercept 1 33.83837 2.44065 13.86 <.0001size 1 -0.10153 0.01305 -7.78 <.0001stock 1 8.13125 3.65405 2.23 0.0408sizestock 1 -0.00041714 0.01833 -0.02 0.9821
Insurance Example: Regression 2proc reg data=insurance; model months = size stock;run;
Analysis of Variance
Source DFSum ofSquares
MeanSquare
F Value
Pr > F
Model 2 1504.41333 752.20667 72.50 <.0001Error 17 176.38667 10.37569 Corrected Total 19 1680.80000
Root MSE 3.22113 R-Square 0.8951Dependent Mean 19.40000 Adj R-Sq 0.8827
Parameter Estimates
Variable DFParameter
EstimateStandard
Errort Value Pr > |t|
Intercept 1 33.87407 1.81386 18.68 <.0001size 1 -0.10174 0.00889 -11.44 <.0001stock 1 8.05547 1.45911 5.52 <.0001
Insurance Example: Comparison
interaction Y R2 adj R2
yesMut: 33.84 – 0.102 size
0.8951 0.8754Stock: 41.97 – 0.102 size
noMut: 33.87 – 0.102 size
0.8951 0.8827Stock: 41.93 – 0.102 size
Insurance Example: Regression 2proc reg data=insurance; model months = size stock;run;
Analysis of Variance
Source DFSum ofSquares
MeanSquare
F Value
Pr > F
Model 2 1504.41333 752.20667 72.50 <.0001Error 17 176.38667 10.37569 Corrected Total 19 1680.80000
Root MSE 3.22113 R-Square 0.8951Dependent Mean 19.40000 Adj R-Sq 0.8827
Parameter Estimates
Variable DFParameter
EstimateStandard
Errort Value Pr > |t|
Intercept 1 33.87407 1.81386 18.68 <.0001size 1 -0.10174 0.00889 -11.44 <.0001stock 1 8.05547 1.45911 5.52 <.0001
Insurance Example: Regression Lines
title2 h=2 'with straight lines';symbol1 v=M i=rl c=black;symbol2 v=S i=rl c=red;proc gplot data=insurance; plot months*size=stock/haxis=axis1 vaxis=axis2;run;
Surgical Example (nknw334.sas)
Surgical unit wants to predict survival in patients undergoing a specific liver operation.
n = 54Y = post-operation survival timeExplanatory Variables
X1: blood clotting score (blood)
X2: prognostic index (prog)
X3: enzyme function test score (enz)
X4: liver function test score (liver)
Surgical Example: inputdata surgical; infile 'I:\My Documents\Stat 512\CH09TA01.txt'
delimiter='09'x;input blood prog enz liver age gender alcmod alcheavy surv
logsurv;run;
proc print data=surgical; run;
title1 h=3 'Original model';title2 h=2 'Matrix Scatterplot';proc sgscatter data=surgical; matrix surv blood prog enz liver;run;
Surgical Example: Diagnosticsproc reg data=surgical; model surv = blood prog enz liver; output out=diag r=resid p=pred;run;
title1 h=3 'Original model';title2 h=2 'Residual plot vs predicted value';axis1 label=(h=2);axis2 label=(h=2 angle=90);symbol1 v=circle;proc gplot data=diag; plot resid*pred/vref=0 haxis=axis1 vaxis=axis2;run;
title2 'Normal plot for residuals';proc univariate data=diag noprint; histogram resid/normal kernel; qqplot resid/normal (sigma=est mu=est);run;
Surgical Example: Y transformationproc transreg data=surgical; model boxcox(surv/lambda=-1 to 1 by 0.1) = identity (blood) identity (prog) identity (enz) identity (liver);run;
Surgical Example: Diagnostics 2data surgical; set surgical; lsurv=log(surv);proc reg data=surgical; model lsurv=liver blood prog enz /ss1 ss2; output out=diagtr r=residtr p=predtr;title1 h=3 'Transformed model with ln Y';title2 h=2 'Residual plot vs predicted value';symbol1 v=circle;proc gplot data=diagtr; plot residtr*predtr/vref=0;run;title2 'Normal plot for residuals';proc univariate data=diagtr noprint; histogram residtr/normal kernel; qqplot residtr/normal (sigma=est mu=est);
Surgical Example: Scatterplot transformed
title2 h=2 'Matrix Scatterplot';proc sgscatter data=surgical; matrix lsurv blood prog enz liver;run;
Surgical Example: Correlationproc corr data=surgical noprob;
var lsurv blood prog enz liver;run;
Pearson Correlation Coefficients, N = 54
lsurv blood prog enz liver
lsurv 1.00000 0.24633 0.47015 0.65365 0.64920
blood 0.24633 1.00000 0.09012 -0.14963 0.50242
prog 0.47015 0.09012 1.00000 -0.02361 0.36903
enz 0.65365 -0.14963 -0.02361 1.00000 0.41642
liver 0.64920 0.50242 0.36903 0.41642 1.00000
Surgical Example: Model Selection – data for the current model
proc reg data=surgical outtest=mparam; model lsurv=blood prog enz liver/ rsquare adjrsq cp press aic sbc;run;proc print data=mparam; run;
Obs _MODEL_ _TYPE_ _DEPVAR_ _RMSE_ _PRESS_
1 MODEL1 PARMS lsurv 0.25088 4.06875
Obs _IN_ _P_ _EDF_ _RSQ_ _ADJRSQ_ _CP_ _AIC_ _SBC_
1 4 5 49 0.75914 0.73948 5 -144.587 -134.642
Obs Intercept blood prog enz liver lsurv
1 3.85193 0.083739 0.012671 0.015627 0.032056 -1
Surgical Example: Model Selection – all subset selection
proc reg data=surgical; model lsurv=blood prog enz liver/ selection=rsquare adjrsq cp b best=3;run;
Surgical Example: Model Selection – all subset selection (cont)
Number in Model
R-Square Adjusted R-Square
C(p) Parameter Estimates
Intercept blood prog enz liver
1 0.4273 0.4162 66.5181 5.26489 . . 0.01512 .
1 0.4215 0.4103 67.6959 5.61241 . . . 0.29812
1 0.2210 0.2061 108.4692 5.56592 . 0.01367 . .
2 0.6632 0.6500 20.5228 4.35094 . 0.01413 0.01538 .
2 0.5992 0.5835 33.5362 5.02882 . . 0.01072 0.20945
2 0.5484 0.5307 43.8729 4.54673 0.10794 . 0.01633 .
3 0.7572 0.7427 3.3879 3.76644 0.09547 0.01334 0.01644 .
3 0.7177 0.7007 11.4343 4.40617 . 0.01101 0.01260 0.12973
3 0.6119 0.5886 32.9601 4.78212 0.04485 . 0.01219 0.16356
4 0.7591 0.7395 5.0000 3.85193 0.08374 0.01267 0.01563 0.03206
Surgical Example: Model Selection – all subset selection (cont)
Number inModel
R-Square AdjustedR-Square
C(p) Variables in Model
1 0.4273 0.4162 66.5181 enz1 0.4215 0.4103 67.6959 liver1 0.2210 0.2061 108.4692 prog2 0.6632 0.6500 20.5228 prog enz2 0.5992 0.5835 33.5362 enz liver2 0.5484 0.5307 43.8729 blood enz3 0.7572 0.7427 3.3879 blood prog enz3 0.7177 0.7007 11.4343 prog enz liver3 0.6119 0.5886 32.9601 blood enz liver4 0.7591 0.7395 5.0000 blood prog enz liver
proc reg data=surgical; model lsurv=blood prog enz liver/ selection=rsquare adjrsq cp best=3;run;
Surgical Example: Type II SSproc reg data=surgical; model lsurv=blood prog enz liver/ss1 ss2; output out=diagtr r=residtr p=predtr;run; Parameter Estimates
Variable DF Parameter Estimate
Standard Error
t Value Pr > |t| Type I SS Type II SS
Intercept 1 3.85193 0.26626 14.47 <.0001 2233.00123 13.17242
blood 1 0.08374 0.02883 2.90 0.0055 0.77696 0.53086
prog 1 0.01267 0.00231 5.47 <.0001 2.59042 1.88571
enz 1 0.01563 0.00210 7.44 <.0001 6.32862 3.48424
liver 1 0.03206 0.05147 0.62 0.5363 0.02442 0.02442
Surgical Example: Model Selection - automatic
proc reg data=surgical; model lsurv=blood prog enz liver / selection=stepwise;run;
All variables left in the model are significant at the 0.1500 level. No other variable met the 0.1500 significance level for entry into the model.
Summary of Stepwise Selection
Step Variable Entered
Variable Removed
Number Vars In
Partial R-Square
Model R-Square
C(p) F Value Pr > F
1 enz 1 0.4273 0.4273 66.5181 38.79 <.0001
2 prog 2 0.2359 0.6632 20.5228 35.72 <.0001
3 blood 3 0.0941 0.7572 3.3879 19.37 <.0001
Surgical Example: Model Selection – backward elimination
Bounds on condition number: 1.0308, 9.1864 All variables left in the model are significant at the 0.1000 level.
Variable Parameter Estimate
Standard Error
Type II SS F Value Pr > F
Intercept 3.76644 0.22676 17.15229 275.89 <.0001
blood 0.09547 0.02169 1.20436 19.37 <.0001
prog 0.01334 0.00203 2.67403 43.01 <.0001
enz 0.01644 0.00163 6.32862 101.80 <.0001
Summary of Backward Elimination
Step Variable Removed
Number Vars In
Partial R-Square
Model R-Square
C(p) F Value Pr > F
1 liver 3 0.0019 0.7572 3.3879 0.39 0.5363